System for the noninvasive estimation of relative age

ABSTRACT

Noninvasive instrumentation and procedures have been developed for estimating the apparent age of human and animal subjects based on the irradiation of skin tissue with near-infrared light. The method of age estimation provides additional information about primary sources of systematic tissue variability due to chronological factors and environmental exposure. Therefore, categorization of subjects on the basis of the estimated apparent age is suitable for further spectral analysis and the measurement of biological and chemical compounds, such as blood analytes. Furthermore, age determination of subjects has particular benefit in assessment of therapies used to reduce the effects of ageing in tissue and measurement of tissue damage.

This application is a continuation-in-part of S. Malin, T. Ruchti, AnIntelligent System for Noninvasive Blood Analyte Prediction, U.S. patentapplication Ser. No. 09/359,191, filed Jul. 22, 1999, which claimspriority from Provisional Patent Application No. 60/116,883, filed Jan.22, 1999.

BACKGROUND OF THE INVENTION

1. Technical Field

The invention relates to the estimation of the apparent age of in vivoskin tissue. More particularly, the invention relates to theinstrumentation and method by which the age and general tissueparameters of subjects can be estimated and classified throughnoninvasive tissue measurements.

2. Description of the Prior Art

Near-infrared (NIR) tissue spectroscopy is a promising noninvasivetechnology that bases measurements on the irradiation of a tissue sitewith NIR energy in the 700-2500 nanometer wavelength range. The energyis focused onto an area of the skin and propagates according to thescattering and absorption properties of the skin tissue. Thus, thereflected or transmitted energy that escapes and is detected providesinformation about the tissue volume encountered. Specifically, theattenuation of the light energy at each wavelength is a function of thestructural properties and chemical composition of the tissue. Tissuelayers, each containing a unique heterogeneous particulate distribution,affect light absorbance through scattering. Chemical components such aswater, protein, fat and blood analytes absorb light proportionally totheir concentration through unique absorption profiles or signatures.The measurement of tissue properties, characteristics or composition isbased on detecting the magnitude of light attenuation resulting from itsrespective scattering and/or absorption properties. The chronologicalage and type and duration of environmental exposure of skin tissue havea pronounced effect on the properties of tissue and is a primary factorin tissue variability between individuals. See, for example, W. Andrew,R. Behnke, T. Sato. Changes with advancing age in, the cell populationof human dermis, Gerontologia, vol. 10, pp. 1-19, (1964/65); W.Montagna, and K. Carlisle. Structural changes in ageing human skin, TheJournal of Investigative Dermatology, vol. 73, pp. 47-53, 1979. J.Brocklehurst, Textbook of Geriatric Medicine and Gerontology, ChurchillLivingstone, Edinburgh and London, pp.593-623 (1973).

Therefore, NIR tissue spectroscopy can be used to detect, quantify, andmonitor age related effects in tissue through a noninvasive measurementprocess. Moreover, NIR tissue spectroscopy has particular benefit inseveral areas including estimation of blood analytes, assessment andmonitoring of therapies. used to reduce the effects of ageing in tissueand diagnosis and quantification of tissue damage.

Blood Analyte Prediction

While noninvasive prediction of blood analytes, such as blood glucoseconcentration, has been pursued through NIR spectroscopy, the reportedsuccess and product viability has been limited by the lack of a systemfor compensating for structural variations between individuals thatproduce dramatic changes in the optical properties of the tissue sampleSee, for example, O. Khalil. Spectroscopic and clinical aspects ofnon-invasive glucose measurements, Clin Chem (1999) vol. 45, pp.165-77,and J. Roe and B. Smoller, Bloodless Glucose Measurements, CriticalReviews in Therapeutic Drug Carrier Systems, vol. 15, no. 3, pp. 199-241(1998).

These differences are largely anatomical and provide distinct systematicspectral absorbance features or patterns that can be related directly tospecific characteristics such as dermal thickness, protein levels,structure of collagen bundles, dermal thinning, hydration, flattening ofthe epidermal-dermal junction and thickness of the subcutaneous layer.While the absorbance features are repeatable by subject over shortperiods of time, over a population of subjects they produce confoundingnonlinear spectral variation. In addition, the changes of skin tissue ofan individual as the result of chronological ageing and/or environmentalexposure lead to profound differences in the volume of tissue sampled bythe NIR measurement device. Therefore, differences between subjects andwithin subjects over time are a significant obstacle to the noninvasivemeasurement of blood analytes through NIR spectral absorbance.

Previously, in the parent application to the current application, S.Malin and T. Ruchti, An Intelligent System For Noninvasive Blood AnalytePrediction. U.S. patent application Ser. No. 09/359,191, filed Jul. 22,1999 an apparatus and procedure for substantially reducing this problemby classifying subjects according to major skin tissue characteristicsprior to blood analyte prediction was disclosed. The selectedcharacteristics are representative of the properties of the actualtissue volume irradiated and the amount of the target analyte that issampled. By grouping individuals according to the similarity of spectralcharacteristics representing the tissue structure, the nonlinearvariation described above is reduced and estimation of blood analytesbecomes more accurate. Specifically, classification of NIR spectral dataaccording to the apparent age or condition of the tissue will improvethe accuracy and robustness of models for estimating tissue/bloodparameters, such as blood analytes, through the significant reduction ofsample variability without the addition of other measurement devices(see S. Malin and T. Ruchti, An Intelligent System For Noninvasive BloodAnalyte Prediction, U.S. patent application Ser. No. 09/359,191, filedJul. 22, 1999).

Apparent Ageing of Skin Tissue

The effects of ageing on skin tissue include two separate phenomena:chronological and photo ageing. Chronological ageing is typified bynatural changes in the skin over time, such as dermal thinning, changesin level of hydration, flattening of the epidermal-dermal junction andreduced sebum/sweat production.

For example, see A. Oikarinen, Ageing of the skin connective tissue: howto measure the biochemical. and mechanical properties. of ageing dermis,Photodermatology Photoimmunology & Photomedicine (1994) vol. 10, pp.47-52; N. Fenske, and C. Lober, Structural and functional changes ofnormal ageing skin, J Am Acad Dermatol (1996) vol. 15, pp. 571-583; M.Gniadecka, and G. Jemec, Quantitative evaluation of chronological ageingand photo ageing in vivo: studies on skin echogenicity and thickness, BrJ Dermatol (1998) vol. 139, pp. 815-821.

Photo ageing is an alteration or damaging of skin as a result of sunexposure, manifested by dryness, solar elastosis, irregular pigmentationand fine wrinkling, and is the cause of premature ageing of skin. See,for example R. Stern, The Measure of Youth, Arch Dermatol (1992) vol.128, pp. 390-393.

Ultrasound has been used to reveal that changes in the upper dermis arerelated to photo ageing and changes in the lower dermis are related tochronological ageing. See A. Oikarinen, Ageing of the skin connectivetissue: how to measure the biochemical and mechanical properties ofageing dermis, Photodermatology Photoimmunology & Photomedicine (1994)vol. 10, pp. 47-52. The upper dermis becomes thicker (solar elastosis)with increased sun exposure and the lower dermis degrades withchronological age. See J. Rigal, C. Escoffier, B. Querleux, B. Faivre,P. Agache, J. Leveque, Assessment of Ageing of the Human Skin by In VivoUltrasonic Imaging, Society for Investigative Dermatology (1989) vol.93, pp. 621-625.

As a result of societal pressure for tanned young skin, pharmaceuticaland cosmetic companies have been developing and marketing products thatclaim to repair the effects of photo-damage to skin and restore skin toits youthful condition. The ability to quantitatively measure theapparent age or condition of tissue is useful in determining theeffectiveness of topical drugs used to reverse damage due to photoageing . See R. Stern, The Measure of Youth, Arch Dermatol, (1992), vol.128, pp. 390-393. An in vivo, quantitative technique would be of greatbenefit in assessing the effectiveness of treatments for photo-damagedtissue. See M. Quan, C. Edwards, and R. Marks, Non-invasive In VivoTechniques to Differentiate Photodamage and Ageing in Hu7man Skin, ActaDerm Venereol, vol. 77, pp. 416-419 (1997).

However, no technique has been reported for quantitatively determiningapparent age on the basis of a noninvasive measurement. Existing methodsfor age determination generally rely on invasive procedures orsubjective evaluation. The amount of photo-damage and the effects ofage-reversing drugs are typically determined by visual inspection of theskin by a trained individual and subsequent assignment of a graderepresenting the degree of damage. Several groups have proposed methodsusing standardized photographs representation the different degrees ofsun damage in an attempt to standardize the age ratings. These methodshave been found to be subjective and not repeatable. Furthermore, thesequalitative methods only provide a surface measurement of theeffectiveness of the product being tested and do not provide anyinformation. about true structural or chemical changes in the tissue.See R. Stern, The measure of Youth, Arch Dermatol, vol. 128, pp. 390-393(1992).

SUMMARY OF THE INVENTION

The present invention provides a method and apparatus for non-invasivelydetermining the apparent age tissue, in vivo. A spectroscopic apparatusin conjunction with an optical interface is used to measure tissueproperties and characteristics that are manifested spectrally and thatvary systematically according to the subject's age and environmentalexposure. A novel method is disclosed that uses in vivo, non-invasiveNIR measurements to estimate the apparent age of the skin and/orclassify the skin according to predefined age group categories.

The procedure for age estimation employs a calibration model that isempirically derived from a set of exemplary samples consisting of NIRtissue measurements and the actual chronological age of a population ofsubjects. The model is a set of parameters and computer generated codethat is implemented to estimate the subject's age. The estimationconsists of an actual age determination in years and one or morerelative property magnitudes that reveal information regarding thetissue properties of the sampled tissue volume.

The apparent age estimate provides a reliable and repeatablequantitative measure of the condition of the skin with respect to thecombined effects of chronological ageing and photo ageing. In addition,magnitude of the two types of ageing is deduced from NIR measurements bytargeting specific tissue volumes and/or decomposing the measurementsthrough multivariate factor analysis to reveal underlying variationcorrelated to specific ageing related tissue parameters such as dermalthickness and hydration. The resulting age estimation and/orclassification is also suitable for categorization of spectral dataprior to blood analyte prediction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a block diagram of an age estimation apparatus andprocedure according to the invention;

FIG. 2 is a plot of a typical NIR absorbance spectrum of an in vivosample of skin tissue;

FIG. 3 provides a block diagram of a general procedure for predictingsubject age based on NIR spectral measurements according to theinvention;

FIG. 4 provides a block diagram of a classification system fordetermining the age of a subject based on NIR spectral measurementsaccording to the invention;

FIG. 5 provides a scatter plot of selected principal component scoresshowing systematic separation by subject age according to the invention;

FIG. 6 is a graph of estimated age versus actual chronological ageaccording to the invention;

FIG. 7 is a block diagram of a first procedure for classification ofsubjects into age categories according to the invention;

FIG. 8 is a block diagram of a second procedure for classification ofsubjects into age categories according to the invention;

FIG. 9 is a plot of the mean second derivative spectra of the separateage categories of FIG. 8 according to the invention;

FIG. 10 is a plot of the mean spectra of the separate age categories ofFIG. 8 according to the invention;

FIG. 11 is a scatter plot showing the separation of principle componentscores associated with young and old subjects according to theinvention; and

FIG. 12 is a plot of a membership function for a fuzzy classification ofsubjects by age according to the invention.

DETAILED DESCRIPTION

The current invention provides an apparatus for measuring the infraredabsorption by tissue irradiated with near-infrared energy, a procedurefor estimating the subject's age and a procedure for classifying thesubject into age related categories for further spectral analysis andblood analyte prediction.

Apparatus

The apparatus includes an energy source, a sensor element, an interface15 to the subject 10, a wavelength selection device and an analyzer. Theenergy source generates and transmits near-infrared energy in thewavelength range 700-2500 nanometers and consists of a device such as anLED array. or a quartz halogen lamp. The method of wavelength separationincludes a monochromator, an interferometer or successive illuminationthrough the elements of an LED array. The optical interface 15 includesa means for transmitting energy 13 from the source to the target skintissue measurement site; for example, a light pipe, a fiber-optic probe,a lens system or a light directing mirror system, and a means forcollecting energy 14 from the target site. Energy is collected from thesurrounding tissue areas in reflectance mode at an optimally determineddistance(s) through the use of starring detectors or fiber-optic probes.Alternately, energy is collected in transmission mode through a skinflap, ear lobe, finger or other extremity. The sensing elements aredetectors that are responsive to the targeted wavelengths the collectedlight is converted to a voltage and sampled through an analog-to-digitalconverter for analysis on a microprocessor-based system.

A block diagram of the integrated system is shown in FIG. 1. In thepreferred embodiment the instrument employs a quartz halogen lamp 11, amonochromator 12 and InGaAs detectors 16. The detected intensity fromthe sample is converted to a voltage through analog electronics 16 anddigitized through a 16-bit A/D converter 17. The spectrum is passed tothe age estimation procedure for processing. First, the absorbance iscalculated 19 on the basis of the detected light through—log(R/R_(o))where R is the reflected light and R_(o) is the light incident on thesample determined by scanning a reference standard. For example, FIG. 2shows a typical absorbance spectrum collected on an apparatus accordingto the preferred embodiment. Subsequent processing steps, describedbelow, result in either an apparent age estimate 23 or an apparent ageclassification 41.

Alternately, the measurement can be accomplished with existing NIRspectrometers that are commercially available including a PerstorpAnalytical NIRS 5000 spectrometer or a Nicolet Magna-IR 760spectrometer. In addition, the measurement can be made by collectingreflected light off the surface of the skin or light transmitted througha portion of the skin, such as the finger or the ear lobe. Further, theuse of reflectance or transmittance can replace the preferred absorbancemeasurement.

Optical Interface

The specific tissue layer sampled is related to the type of ageing. Forexample, degradation of the upper dermis is related to photo ageingwhile chronological ageing is distinguished by changes in the lowerdermis. See, for example M. Gniadecka and G. Jemec, Quantitativeevaluation of chronological ageing and photo ageing in vivo: studies onskin echogenicity and thickness, Br J Dermatol (1998) vol. 139, pp.815-821.

The upper dermis becomes thicker (solar elastosis) with increased sunexposure and the lower dermis degrades with chronological age. In thepresent invention the specific tissue layer that is sampled iscontrolled through the spacing of the point of illumination and thepoint of light detection by the optical interface (15). The point ofillumination is set through a focusing lens or delivered directly via afiber optic probe. The point of detection is controlled through starringoptics or a fiber optic probe. The preferred spacing for thedetermination of photo ageing is less than 300 μm. The preferred sourceto illumination spacing for chronological ageing is 0.3 to 3 mm based onthe wavelength region (0.3 mm between 2000 and 2500 nm, 1-2 mm in the1500-1850 nm range and 3 mm in the 700-1400 nm range).

General Age Estimation Procedure

The general procedure for age estimation based on the measured NIRspectrum, shown in FIG. 3, is implemented in a microprocessor 18 thatautomatically receives the measurement information from the ADC 17. Theprocedure for age estimation includes the sub-procedures outlierdetection 20, preprocessing 21 and estimation 23. Each sub-procedure isperformed on the basis of a calibration set of exemplary measurementsthat includes an absorbance spectrum and a measure of the apparent age(or chronological age or photo-age) of the subject population. Presentedbelow is an overview of the procedure for estimating the apparent age onthe basis of spectral measurements. Further details are provided in thesubsequent Implementation section, below.

Measurement

The measurement 30 is a spectrum denoted by the vector mε^(N) ofabsorbance values pertaining to a set of N wavelengths λε^(N) that spanthe near infrared (700 to 2500 nm). A typical plot of m versus λ isshown in FIG. 2.

Outlier Detection

The outlier detection procedure 20 is a method for detecting invalidmeasurements due to spectral variations resulting from problems in theinstrument, poor sampling of the subject or a subject lying outside thecalibration set. The preferred method for the detection of spectraloutliers is through a principal component analysis and a furtheranalysis of the resulting residuals. First, the spectrum m is projectedonto five eigenvectors, contained in the matrix o, that were previouslydeveloped through a principal component analysis (on a calibration setof exemplary absorbance spectra) and are stored in the computer systemof the device. The calculation is given by $\begin{matrix}{{{xpc}_{o} = {\sum\limits_{k = 1}^{7}\quad {mo}_{k}}},} & (1)\end{matrix}$

and produces the 1 by 5 vector of scores, xpc_(o) where o_(k) is thek^(th) column of the matrix o. The residual q is determined according to

q=m−xpc _(o) o ^(T)  (2)

and compared to three times the standard deviation of the expectedresidual (of the calibration set). If greater, the sample is reported tobe an outlier and the age determination procedure is terminated.

Preprocessing

The optional step of preprocessing 21 includes operations such aswavelength selection, scaling, normalization, smoothing, derivatives,filtering and other transformations that attenuate the noise andinstrumental variation without affecting the signal of interest. Thepreprocessed measurement, xε^(N), is determined according to

x=h(λ,m)  (3)

where h:^(N×2)→^(N) is the preprocessing function. Wavelength selectionis performed on the data to eliminate extraneous variables that may biasthe calibration or portions of the measured spectrum with a lowsignal-to-noise ratio. This is performed visually and through ananalysis of the noise at each wavelength. The specific preprocessingmethods used for age estimation may include wavelength selection,multiplicative scatter correction and derivatives. See, for example P.Geladi, D. McDougall and H. Martens. Linearization andScatter-Correction for Near-Infrared Reflectance Spectra of Meat,Applied Spectroscopy (1985) vol. 39, pp. 491-500, and A. Savitzky and M.Golay, Smoothing and Differentiation of Data by Simplified Least SquaresProcedures, Anal. Chem., vol. 36, no. 8, pp. 1627-1639 (1964).

Estimation

The estimation procedure employs a calibration model 31 that maps thepreprocessed spectrum through a linear or nonlinear mapping to anestimate of the age. In the linear case, given the processed spectrum x,and the calibration model coefficients w_(c), the age estimate isdetermined according to $\begin{matrix}{\hat{y} = {\sum\limits_{k = 1}^{N}\quad {w_{c,k}x_{k}}}} & (4)\end{matrix}$

were W_(c,k) is the k^(th) element of w_(c) and ŷ is the age estimate.One skilled in the art will appreciate that a nonlinear mapping from xto ŷ can also be easily specified through artificial neural networks,nonlinear partial-least squares regression or other nonlinear method ofcalibration.

See, for example H. Martens and T. Naes, Multivariate Calibration. NewYork: John Wiley and Sons, (1989) pp. 419; P. Geladi, D. McDougall andH. Martens. Linearization and Scatter-Correction for Near-InfraredReflectance Spectra of Meat, Applied Spectroscopy, (1985) vol. 39 pp.491-500; and Y. Pao, Adaptive Pattern Recognition and Neural Networks,Addison-Wesley Publishing Company, Inc., Reading, Mass., (1989).

The preferred model is linear and is constructed through factor analysisto decompose the high dimensional (redundant) data consisting ofabsorbance, intensity or reflectance measurements at several hundredwavelengths to a few significant factors that represent the majority ofthe variation within the data set. The factors that capture variation inthe spectra that correlate with age are used in the calibration modeland the samples are projected into the resulting factor space to producea set of scores for each sample. Finally, multiple linear regression isapplied to model the relationship between the scores of the significantfactors and the apparent age 23 of the subject.

General Age Classification Procedure

NIR measurements 30 from tissue samples of varying age can be classifiedinto age groups according to their physical and chemical properties. Theprocedure for classifying samples according to predefined age groupsincludes preprocessing 21 the data for feature enhancement, performing afactor analysis for variable reduction and developing a classificationcalibration on the significant factors. The classification calibrationis any mathematical or statistical technique, such as Fisher's LinearDiscriminant Analysis, that assigns a label to a sample and, using adecision rule, can determine the population membership of the sample.Methods, such as multiplicative scatter correction and derivatives, areused to enhance features associated with age parameters.

See R. Duda and P. E. Hart, Pattern Classification and Scene Analysis,John Wiley and Sons, New York, 1973. Also see P. Geladi, D. McDougalland H. Martens. Linearization and Scatter-Correction for Near-InfraredReflectance Spectra of Meat, Applied Spectroscopy, (1985) vol. 39, pp.491-500.

A factor-based analytical method, such as principal component analysis(PCA), is applied to the preprocessed data in order to reduce the dataset down to a few significant age-related factors. The classificationcalibration is developed using the significant age-related factors. Theclassification calibration can then be used to determine the membershipof a new sample. FIG. 4 is a flow diagram of the basic ageclassification steps. The general procedure is implemented in amicroprocessor 18 that automatically receives the measurementinformation from the ADC.

Outlier Detection and Preprocessing

The procedures for outlier detection 20 and preprocessing 21 are similarto those defined in the General Age Estimation Section. The specificprocedures are optimized on the basis of the calibration set for featureextraction 22 and classification 41 as presented in the ImplementationSection, below.

Feature Extraction

Feature extraction 22 determines the salient characteristics ofmeasurements that are relevant for age classification. Featureextraction 22 is any mathematical transformation that enhances a qualityor aspect of the sample measurement for interpretation. The purpose offeature extraction 22 is to concisely represent and enhance theproperties and characteristics of the tissue measurement site for ageclassification. In addition, the features provide significantinformation about the tissue properties they represent and can be usedfor alternate purposes such as system diagnostics or optimization.

The features are represented in a vector, zε^(M) that is determined fromthe preprocessed measurement through

z=f(λ,x)  (5)

where f: ^(N)→^(M) is a mapping from the measurement space to thefeature space. Decomposing f(•) yields specific transformations,f_(i)(•): ^(N)→^(M) _(i) for determining a specific feature. Thedimension, M_(i), indicates whether the i^(th) feature is a scalar or avector, and the aggregation of all features is the vector z. When afeature is represented as a vector or a pattern, it exhibits a certainstructure indicative of an underlying physical phenomenon.

The individual features are divided into two categories:

abstract, and

simple.

Abstract features do not necessarily have a specific interpretationrelated to the physical system. Specifically, the scores of a principalcomponent analysis are useful features although their physicalinterpretation is not always known. (See H. Martens, T. Naes,Multivariate Calibration, John Wiley and Sons, New York (1989) pp. 419.For example, the utility of the principal component analysis is relatedto the nature of the tissue absorbance spectrum. The most significantvariation is generally related to the tissue structure, which variessystematically with age. Therefore, the scores from the principalcomponent analysis constitute a valuable set of features in that theyprovide information that can be used for age determination.

According to a preferred realization of the invention, the use ofprincipal component analysis to represent spectral variation related toage is demonstrated through the Experimental Data Set, more fullydescribed in the Implementation Section, below. The set of spectra,collected on 266 subjects of diverse age and sex, was subjected toprincipal component analysis in two wavelength regions: Region 1(1100-1380 nm) and Region 2 (1550-1800 nm). The scores of selectedprincipal components were plotted versus one another as shown in FIGS.5a, 5 b and 5 c, with different symbols for data points corresponding toindividuals above (old) and below (young) the mean age of 49 years. Thescores, representing variation in the spectra, show a pronouncedsystematic separation according to subject's age. The unmistakablegrouping of the data according to age in the scatter plots of FIG. 5clearly demonstrates the utility of feature extraction through principalcomponent analysis and the possibility of subsequent classification ofthe subjects according to age. In addition, the correlation between ageand the lower numbered principal components indicates that agerepresents a primary source of variation in the spectra.

Simple features are derived from an a priori understanding of the sampleand can be related directly to a physical phenomenon. For example, thethickness of the dermis varies systematically with age and results inspecific spectral manifestations. These spectral variations areextracted and enhanced and serve both as a feature for ageclassification and as a measurement of their respective tissueproperties.

Although the full spectrum can be passed to the classification model 40for age classification 41, a preferred realization of the inventionemploys either of two specific methods of feature extraction thatprovide superior classification performance and measurement of otherrelevant tissue properties:

the scores from factor analysis, and

the estimates from partial least squares (PLS) regression.

The detailed implementation of the procedure for extracting thesefeatures on the basis of a calibration set is provided in the nextsection, below.

Crisp Classification

The classification 41 of the subject's age on the basis of the extractedfeatures is performed through a classification step that involves amapping and a decision. The mapping step is given by

L=f(z)  (6)

where L is a scalar that can be used to measure the distance from thepredefined age categories. For example, two values, L_(old) andL_(young), associated with the representative or mean value of L for the“old” and “young” categories respectively are predefined, and the classassignment is based on the closeness of L to L_(old) and L_(young). Forexample, the distance of L from a previously defined class boundarymeans that classes can be measured by

 d _(old) =|L _(old) −L|

d _(young) =|L _(young) −L|  (7)

The decision is made as follows:

if d_(old)>d_(young) then the apparent age of the tissue is classifiedas “old,”

if d_(old)>d_(young) then the apparent age of the tissue is classifiedas “young.”

The mapping and decision limits are determined from a calibration set ofexemplary features and corresponding apparent age reference valuesthrough a classification calibration procedure. Commonly known methodsof classification calibration include linear Discriminant analysis,SIMCA, k nearest-neighbor, fuzzy classification and various forms ofartificial neural networks. Furthermore, one skilled in the willappreciate that more than two distinct classes for age can be definedwith an upper limit based on the accuracy of the measurement device.

See, for example R. Duda and P. Hart, Pattern Classification and SceneAnalysis, John Wiley and Sons, New York, (1973); S. Wold and M.Sjostrom. SIMCA: A method for analyzing chemical data in terms ofsimilarity and analogy, Chemometrics: Theory and Application, ed. B. R.Kowalski, ACS Symposium Series, vol. 52, (1977); J. Bezdek and S. Pal,eds., Fuzzy Models for Pattern Recognition, IEEE Press, Piscataway,N.J., (1992); J. Keller, M. Gray and J. Givens. A Fuzzy K nearestNeighbor Algorithm, IEEE Transactions on Systems, Man, and Cybernetics,Vol. SMC-15, No. 4, pp. 580-585, (July/August, 1985); and Y. Pao,Adaptive Pattern Recognition and Neural Networks, Addison-WesleyPublishing Company, Inc., Reading, Mass., (1989).

Fuzzy Classification

While statistically based class definitions provide a set of classesapplicable to age classification, the apparent age of a tissue sampleand the resulting spectral variation change over a continuum of values.Consequently, the natural variation in the spectra results in classoverlap. Distinct class boundaries based on age do not exist and manymeasurements are likely to fall between classes and have a statisticallyequal chance of membership in any of several classes. Hence, “hard”class boundaries and mutually exclusive membership functions may beinsufficient to model the variation found in a target population.

A more versatile method of class assignment is based on fuzzy settheory. (See J. Bezdek, and S. Pal, eds., Fuzzy Models for PatternRecognition, IEEE Press, Piscataway, N.J., (1992); C. Chen, ed., FuzzyLogic and Neural Network Handbook, IEEE Press, Piscataway, N.J. (1996);and L. Zadeh, Fuzzy Sets, Inform. Control, vol. 8, pp. 338-353, (1965).

Generally, membership in fuzzy sets is defined by a continuum of gradesand a set of membership functions that map the feature space into theinterval [0,1] for each class. The assigned membership grade representsthe degree of class membership with “1” corresponding to the highestdegree. Thus, a sample can simultaneously be a member of more than oneclass.

The mapping from feature space to a vector of class memberships is givenby

c _(k) =f _(k)(),  (8)

where k=1,2, . . . P, f_(k)(•) is the membership function of the k^(th)class, c_(k)ε[0,1] for all k and the vector cε^(P) is the set of classmemberships. An example of the general equation employed to represent amembership function is $\begin{matrix}{y = ^{\frac{- 1}{2\sigma^{2}}{({z - \overset{\_}{z}})}^{2}}} & (9)\end{matrix}$

where y is the degree of membership in a sub-set, z is the feature usedto determine membership, {overscore (z)} is the mean or center of thefuzzy sub-set and σ is the standard deviation. However, one skilled inthe art will appreciate that the suitable membership function isspecific to the application.

The membership vector provides the degree of membership in each of thepredefined classes and can be used for blood analyte prediction asdisclosed in the parent application to the current application, S.Malin, T. Ruchti, An Intelligent System For Noninvasive Blood AnalytePrediction, U.S. patent application Ser. No. 09/359,191, (Jul. 22,1999). Alternately, the degree of class membership can be used tocalculate the apparent age, photo-age or chronological age of anindividual with a suitable function for defuzzification. Thedefuzzification function can be determined as described by Bezdek, et al(See J. Bezdek, and S. Pal, eds., Fuzzy Models for Pattern Recognition.IEEE Press, Piscataway, N.J., (1992). Also see the parent application tothe current application, as above.) Alternately, a calibration set ofexemplary spectral measurements and associated age reference values canbe used to determine a calibration model for mapping the classmembership to an estimate of the selected age.

Implementation Details

Various realizations of the invention, comprising specific proceduresfor age estimation and classification are described in detail below.

Experimental Data Set

A study was performed to generate calibration and validation data forthe procedures subsequently described. Two Hundred sixty-six humansubjects of diverse age, sex and ethnicity were recruited at a localhealth care facility, and detailed demographic information about eachparticipant was recorded. Four replicate absorbance spectra weremeasured on each subject's forearm with the previously describedspectrometer apparatus according to the preferred embodiment. One sampleper each participant was included in the data set. Henceforth, the totalset of spectra and demographic information shall be referred to as the“Experimental Data Set.”

While this is a specific experiment aimed at the determination of asuitable set for calibrating the age determination apparatus, one willreadily appreciate that for different subjects and for different targetperformance levels other experiments with smaller or larger subjectpopulations would be performed. Moreover, experiments specific to photoageing versus chronological ageing would replace this experiment given adifferent target set of age estimates.

Estimation

Two implementations of the age estimation procedure are described in thefollowing sub-sections differing by the wavelength region of theabsorbance spectrum applied.

Age Estimation 1 (1100-1400 nm)

In the first implementation, the procedure outlined in FIG. 3 wasemployed. The (PCA q-residual) outlier analysis was performed asdescribed above and 36 samples were removed due to unusually highresiduals. No preprocessing was applied and the wavelength region waslimited to 1100-1400 nm to ensure sampling of the lower dermis. Whilesampling of the subcutaneous tissue also occurs in this wavelengthregion, the magnitude of the fat absorption features is indicative ofthe absorption characteristics of the dermis and epidermis and providesan indirect measurement of the target absorption characteristics.Partial least squares (PLS) regression with 17 factors was applied tothe entire data set to develop a calibration model. (See P. Geladi andB. Kowalski, Partial least-squares regression: a tutorial, AnalyticaChimica Acta, vol. 85, pp. 1-17 (1986).

The performance of the estimation model was evaluated throughcross-validation using a “leave-one-out” strategy and calculating thestandard error values. The cross-validation procedure was usediteratively to estimate the age of each sample by using all othersamples to construct the calibration model. After each sample had beenpredicted the standard error of cross-validation (SECV) was computed asthe root mean square error of the cross-validation age estimates. Theresults of the estimated chronological age (through cross-validation)versus the actual age are shown in FIG. 6. The standard error ofprediction is 7.8 years and the plot shows a clear statisticallysignificant level of estimation. The error in prediction may beattributed to two phenomena. First, the rate of chronological ageing indifferent individuals is not necessarily the same. As a result, thereference values contain error by definition. Secondly, photo-damage tothe upper dermis represents a confounding effect that may limit theaccuracy of the estimation model.

Age Estimation 2 (1500-2400 nm)

In this implementation the estimation of age 23 is performed usingpartial least squares (PLS) regression on upper wavelength regions ofthe absorbance spectra. While the lower wavelength region used in AgeEstimation Method One targets primarily the dermis and subcutaneoustissue, the high absorbance of water prevents significant sampling ofthe subcutaneous tissue at upper wavelengths. Since age relatedparameters manifest themselves most distinctly in the dermis andepidermis of the tissue, limiting the wavelength range to the 1500 to2400 nm region limits potential interferences contributed by thesubcutaneous tissue.

Outlier analysis was applied to the data set as previously described.The remaining samples were split into calibration (60%) and test (40%)sets. No feature enhancement or preprocessing techniques were appliedand cross-validation validation was used on the calibration set todetermine that eight was the optimal number of factors for featureextraction. The calibration was then developed using the calibration setand applied to the test set for validation.

The test set had a standard error of prediction of 8.0 years. The causeof the error in the prediction can be explained similarly to that forAge Estimation Method One: a disparity between the actual chronologicalage of the subject and the apparent age of the tissue; consequently, asubject's skin condition may resemble that of the skin of a personseveral years older due to photo ageing. effects. Thus, in a personusing a photo-damage-reversing drug, periodically predicting theapparent age of the skin throughout the course of treatment would be aneffective way to monitor the performance of the drug.

Classification

The classification 41 of subjects according to age was implemented usingthree different approaches. The first two involve a crisp classificationsystem in which distinct class boundaries are defined and each sampledabsorbance spectrum has membership in only one class. The third methodinvolves the use of a fuzzy system to arrive at an estimate of thedegree of membership in each of several predefined classes for eachsample.

In the application of this system to the prediction of blood analytes,the accuracy of chronological age prediction is not the most importantelement but rather the characterization of the apparent age of thesampled tissue volume. Nevertheless, one skilled in the art willappreciate that the following methods and procedures are easily adaptedto other applications.

Age Classification 1. (Crisp 1100-1800 nm)

The first age classification implementation is depicted in FIG. 7 andinvolves the prediction of the subject's age using a linear modeldeveloped through partial least squares regression. In the currentimplementation, the wavelength range of the spectrum 30 is truncated tothe regions 1100-1380 nm and 1550-1800 nm in the wavelength selectionprocess 50. Next, the subject's age is predicted through a calibrationmodel. The model, developed through PLS on a calibration set ofexemplary samples, consists of a set of coefficients contained in thevector w 51 and is applied as shown in FIG. 7 to produce the ageprediction, a 52. The subject is classified as “young” 54 or “old” 53 bycomparing a 52 to the mean age {overscore (a)}=49 as detailed in thefigure. One skilled in the art will appreciate that more than twodistinct classes can be defined with an upper limit based on theaccuracy of the measurement device.

Using the Experimental Data Set, the PLS calibration model was developedusing 20 factors. The classification accuracy was evaluated throughcross-validation in which groups of 12 samples were iteratively left outof the calibration model. The accuracy of age classification was foundto be 79%. The reason for the error is attributable to the continuum ofchronological ages and skin tissue properties that were measured using aclassification with crisp boundaries. In Age Classification 2 theboundary is modified by removing samples from the calibration thatrepresent chronological ages in between the two main old and youngcategories.

Age Classification 2 (Crisp 1500-2500 nm)

The second age classification procedure is detailed in FIG. 8 and wasdeveloped based on the calibration set described in the Age Estimation 2Section. The procedure provides two groups: a young group that includesages from 20 to 39 years old and an old group which ranged from 60 to 84years old. The middle age range was left out in order to get a moredistinct separation between age groups. A 33-point second Savitzky-Golayderivative 60 (see A. Savitzky and M. Golay. Smoothing andDifferentiation of Data by Simplified Least Squares Procedures, Anal.Chem., vol. 36, no. 8, pp. 1627-1639, (1964). was applied to thespectral data 30 to enhance the variation in the mean spectra 70, 71 dueto age (see FIG. 9). Referring now to FIG. 10, the largest separationbetween the mean spectra 70, 71 of the two age groups was observed overthe wavelength regions of 1500 to 2500 nm. Thus, this was the wavelengthregion selected 51 for further analysis. A principal component analysis(PCA) was applied to the spectral data 30 over the selected wavelengthregion. The scores from the PCA were plotted against one another toreveal clustering of the data related to age. As shown in FIG. 11, twoclusters corresponding to the two age groups formed when the scores fromprincipal component one was plotted against scores from principalcomponent four 61. These two groups can be used to represent tissue withyoung and old characteristics. Using these two distinct age groups aclassification model was developed from the age significant scores andused to classify the apparent age of new samples.

The procedure of FIG. 8 uses two eigenvectors associated with principalcomponents 1 and 4 to determine the scores, xpc₁ and xpc₂, of eachsample. A Discriminant function is applied to classify the subjectsbased on the two features contained in m through the equation shown inthe figure to produce the scalar, L 63. This result is compared to{overscore (L)} 64, the center between the two classes. If L>{overscore(L)} then the subject is classified as a old 65. If not, the spectrum isclassified as belonging to the young class 66. Using the calibrationset, the linear mapping 62 of FIG. 8 was developed via linearDiscriminant analysis (see Duda, R. O. and P. E. Hart, PatternClassification and Scene Analysis, John Wiley and Sons, New York, 1973)and. produced the following weight vector

 w=[0.1198 0.9928]  (10)

From the calibration set, the mean value for L was found to be 0.1131.Using these parameters in conjunction with the procedure of FIG. 8allowed age prediction in the test set with 90% accuracy.

Age Classification 3 (Fuzzy)

The final procedure for age classification is the utilization of a setof fuzzy membership functions that determine the degree of membership inthe categories young, middle aged and old based on the feature definedin the Age Classification 1 Section. The set of membership functions91,92,93 shown in FIG. 12, are Gaussian functions given by$\begin{matrix}{y = ^{\frac{- 1}{2\sigma^{2}}{({z - \overset{\_}{z}})}^{2}}} & (11)\end{matrix}$

where y is the degree of membership in a sub-set, z is the feature usedto determine membership (the PLS age prediction), {overscore (z)} is themean or center of the fuzzy sub-set and σ is the standard deviation.However, one skilled in the art will appreciate that the suitablemembership function is specific to the application. The mean andstandard deviation associated with each of the three categories weredetermined based on the target population of subjects in theExperimental Data Set and are listed in Table 1.

TABLE 1 Parameters of Membership Functions plotted in FIG. 12. StandardMean Deviation Sub-Set Category (Years) (Years) Young 30 7 Middle Aged50 10 Old 70 7

Values for the feature inputs to the membership functions that areunusually high or low fall outside that expected range of the sub-setsand are assigned low membership values. This information is used toindicate that the subject's apparent age is outside of the previouslyexamined population and is used for outlier analysis. For the currentimplementation, when y<0.1 for all sub-sets, the prediction is assigneda low confidence level.

The resulting class memberships are suitable for use in categorizationfor blood analyte prediction as described in the parent application tothe current application: An intelligent system for noninvasive bloodanalyte prediction, S. Malin, T. Ruchti, U.S. patent application Ser.No. 09/359,191, filed Jul. 22, 1999. The membership functions describedhave been designed for a specific population of subjects and cannot begeneralized to all potential individuals. The invention, nevertheless,is directed to the arbitrary use of membership functions to assign adegree of membership in a given class to a subject for blood analyteprediction.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the Claims includedbelow.

What is claimed is:
 1. A method for noninvasively determining theapparent age of a tissue sample in vivo, comprising the steps of:measuring the NIR absorption spectrum of said tissue sample; detectingoutliers, wherein said outliers are invalid measurements caused byspectral variation due to any of instrument malfunction, poor sampling,and subjects outside of a calibration set; preprocessing, wherein saidpreprocessing step includes one or more transformations that attenuatenoise, and instrumental variation without affecting the signal ofinterest, including wavelength selection, scaling, normalizationsmoothing derivatives, and filtering; and estimating the apparent age ofsaid tissue sample based on said NIR spectrum.
 2. The method of claim 1,wherein said NIR region comprises a range of about 700 to about 2500 nm.3. The method of claim 1, wherein said spectrum is denoted by a vectormε^(N) of absorbance values pertaining to a set of N wavelengths λε^(N).4. The method of claim 1, wherein said outlier detection step employsprincipal components analysis and residual analysis to detect spectraloutliers.
 5. The method of claim 4, wherein said outlier detection stepfurther comprises the steps of: projecting a spectrum m onto a pluralityof eigenvectors, contained in a matrix o, said matrix o being previouslydeveloped through principal components analysis of said calibration set,where ${{xpc}_{o} = {\sum\limits_{k = 1}^{7}\quad {mo}_{k}}},$

and where o_(k) is the k^(th) column of the matrix o; determining theresidual q, according to q=m−xpc _(o) o ^(T) comparing said residual qto three times the standard deviation of the residual of saidcalibration set; and reporting said sample as an outlier if q isgreater.
 6. The method of claim 1, wherein said preprocessedmeasurements are denoted by a vector Xε^(N), and wherein said vector isdetermined according to x=h(λ,m) and wherein a preprocessing function ish:^(N×2)→^(N).
 7. The method of claim 1, wherein said age estimationstep further comprises the step of: providing a calibration model to mapsaid preprocessed spectrum through a mapping to an estimate of age. 8.The method of claim 7, wherein said calibration model comprises NIRtissue measurements and chronological ages of an exemplary subjectpopulation.
 9. The method of claim 7, wherein said mapping is linear.10. The method of claim 7, wherein said age estimate is determinedaccording to${\hat{y} = {\sum\limits_{k = 1}^{N}\quad {w_{c,k}x_{k}}}};$

given the preprocessed spectrum x, and the calibration model w_(c),where W_(c,k) is the k^(th) element of w_(c) and is the age estimate.11. The method of claim 10, wherein said calibration model employsfactor analysis to decompose a high-dimensional (redundant) data setcomprising absorbance, intensity or reflectance measurements at aplurality of wavelengths to significant factors representing themajority of variation within said data set; and wherein said calibrationmodel includes factors that capture variation in said spectra correlatedwith variation in subject age.
 12. The method of claim 10, furthercomprising the steps of; projecting said samples into a resulting factorspace to produce a set of scores for each sample; and applying multiplelinear regression to model the relationship between said scores andapparent age of said subject.
 13. The method of claim 7, wherein saidmapping is non-linear.
 14. The method of claim 13, wherein saidnon-linear mapping is specified through any of artificial neuralnetworks and non-linear partial least squares regression.
 15. A methodfor non-invasively determining the apparent age of a tissue sample invivo comprising the steps of: providing a calibration set of exemplarymeasurements; measuring the NIR absorption spectrum of said tissuesample; detecting outliers, wherein said outliers are invalidmeasurements caused by spectral variation due to any of instrumentmalfunction, poor sampling, and subjects outside of said calibrationset; and estimating the apparent age of said tissue sample based on saidNIR absorption spectrum.
 16. A method of classifying a subject accordingto age based on noninvasive NIR measurements, comprising the steps of:providing a calibration set of exemplary measurements; measuring the NIRabsorption spectrum of said tissue sample; detecting outliers, whereinsaid outliers are invalid measurements caused by spectral variation dueto any of instrument malfunction, poor sampling, and subjects outside ofsaid calibration set; preprocessing, wherein said preprocessing stepincludes at least one transformation that attenuates noise andinstrumental variation without affecting the signal of interest,including wavelength selection, scaling, normalization, smoothing,derivatives, and filtering, extracting features, whereby factors ofmeasurements relevant to age classification are determined; andclassifying said sample according to predefined age groups.
 17. Themethod of claim 16, wherein said feature extraction step comprises anymathematical transformation that enhances a quality or aspect of saidsample measurement for interpretation to represent concisely theproperties and characteristics of the tissue measurement site for ageclassification.
 18. The method of claim 17, wherein said featureextraction step employs scores from factor analysis.
 19. The method ofclaim 17, wherein said feature extraction step employs partial leastsquares regression.
 20. The method of claim 17, wherein said featuresare represented in a vector, zε^(M) that is determined from apreprocessed measurement through: z=f(λ,x) where f.^(n)→^(m) is amapping from a measurement space to a feature space, wherein decomposingf(•) yields specific transformations, f(•):^(n)→^(m) _(i) fordetermining a specific feature, wherein the dimension M_(i) indicateswhether an i^(th) feature is a scalar or a vector and an aggregation ofall features is the vector z, and wherein a feature exhibits a certainstructure indicative of an underlying physical phenomenon when saidfeature is represented as a vector or pattern.
 21. The method of claim20, wherein individual features are divided into two categoriescomprising: abstract features that do not necessarily have a specificinterpretation related to a physical system; and simple features thatare derived from an a priori understanding of a sample and that can berelated directly to a physical phenomenon.
 22. The method of claim 21,wherein spectral variations due to changes in dermal thicknesscorresponding with age are extracted and enhanced, and wherein saidvariations serve as a feature for age classification and measurement oftissue properties.
 23. The method of claim 18, further comprising thestep of: employing factor-based methods to build a model capable ofrepresenting variation in a measured absorbance spectrum related tosubject age, wherein projection of a measured absorbance spectrum ontosaid model constitutes a feature that represents spectral variationrelated to subject age.
 24. The method of claim 16, wherein saidclassification step further comprises the steps of: measuring thesimilarity of at least one feature to predefined age categories; andassigning membership to one of said predefined categories.
 25. Themethod of claim 24, wherein said assigning step uses mutually exclusiveclasses and assigns each sample to one class.
 26. The method of claim24, wherein said assigning step uses a fuzzy classification system thatallows class membership in more than one class simultaneously.
 27. Themethod of claim 25, wherein said assigning step further comprises thesteps of: mapping said sample to one of said predefined classes; andapplying a decision rule to assign class membership.
 28. The method ofclaim 27, wherein said mapping step is given by: L=f(z) where L is ascalar that measures distance of a sample from the predefined agecategories.
 29. The method of claim 28, wherein said age categories are“old” and “young” and where L_(old) corresponds to a representativevalue for said “old” class and L_(young) corresponds to a representativevalue for said “young class”; and wherein said class assignment is basedon the closeness of L to L_(old) and L_(young).
 30. The method of claim29, wherein a distance d_(old) of L to L_(old) is measured by d _(old=|)L _(old−) L _(|), and wherein a distance d_(young) of L to L_(young) ismeasured by d _(young=|) L _(young−) L _(|).
 31. The method of claim 29,wherein said decision rule is: if d_(old)<d_(young), then the apparentage of the sample is classified as “old;” and if d_(old)≧d_(young), thenthe apparent age of the sample is classified as “young”.
 32. The methodof claim 27, wherein limits for said mapping and decision rule aredetermined from a calibration set of exemplary measurements andcorresponding apparent age reference values through a classificationcalibration procedure.
 33. The method of claim 32, wherein saidclassification calibration procedure comprises any of linearDiscriminant analysis, SIMCA, k nearest neighbor, fuzzy classificationand artificial neural networks.
 34. The method of claim 26, whereinclass membership is defined by a continuum of grades, and wherein a setof membership functions map a feature space into an interval [0,1] foreach class and wherein an assigned grade represents a degree of classmembership, and wherein a grade of “1” represents the highest degree ofclass membership.
 35. The method of claim 34, wherein the mapping fromthe feature space to a vector of class memberships is given by: c_(k) =f_(k)(z) where k=1,2, . . . P, and where f_(k)(•) is the membership ofthe k^(th) class, and where ck_(k)ε[0,1] for all k, and where the vectorcε^(P) is the set of class memberships.
 36. The method of claim 35,wherein a membership function is represented by${y = ^{\frac{- 1}{2\sigma^{2}}{({z - \overset{\_}{z}})}^{2}}},$

where y is the degree of membership in a fuzzy sub-set, z is the featureused to determine membership is the center of a fuzzy subset, and σ isthe standard deviation.
 37. The method of claim 35, wherein saidmembership vector provides the degree of class membership in each ofsaid predefined classes.
 38. The method of claim 1, further comprisingthe step of performing a blood analyte prediction based on said ageestimate.
 39. The method of claim 15, further comprising the step ofperforming a blood analyte prediction based on said age estimate. 40.The method of claim 16, further comprising the step of performing ablood analyte prediction based on said age classification.
 41. Anapparatus for non-invasively estimating the relative age of a subjectcomprising: means for generating near infrared (NIR) energy; means forseparating said generated NIR energy into a plurality of wavelengthregions; an optical interface comprising: means for transmitting saidNIR energy from said wavelength separating means towards a targetmeasurement site on a subject; and means for collecting NIR energyemanating from said measurement site; means for detecting said collectedenergy and converting said collected energy to a voltage; means forconverting said voltage to a digital value; and means for analyzing saiddigital value whereby said analysis results in an estimate of saidsubject's relative age.
 42. The apparatus of claim 41, wherein saidenergy source transmits light in the wavelength range of about 700 nm to2500 nm.
 43. The apparatus of claim 42, wherein said energy source is anLED array or a quartz halogen lamp.
 44. The apparatus of claim 41,wherein said wavelength separating means is a monochromator or aninterferometer.
 45. The apparatus of claim 41, wherein said wavelengthseparating means comprises successive illumination through an LED array.46. The apparatus of claim 41 wherein said transmission means is a lightpipe, a fiber-optic probe, a lens system, or a light-directing mirrorsystem.
 47. The apparatus of claim 41, wherein said energy collectingmeans comprises at least one starring optical detector.
 48. Theapparatus of claim 41, wherein said energy collecting means comprises atleast one fiber-optic probe.
 49. The apparatus of claim 41, wherein saidenergy detecting means comprises InGaAs detectors.
 50. The apparatus ofclaim 41, wherein said digitizing means is a 16-bit A/D converter. 51.The apparatus of claim 41, wherein said optical interface is positionedat optimally determined distances from said target measurement site. 52.The apparatus of claim 51, wherein said distances are specifiedaccording to layer of the skin tissue to be sampled.
 53. The apparatusof claim 52, wherein said optimal distance is approximately 0-300 μm forthe upper dermis.
 54. The apparatus of claim 52, wherein said optimaldistance is approximately 0.3-3 mm for the lower dermis.
 55. Theapparatus of claim 51, wherein a point of illumination is set throughany of a focusing lens and a fiber-optic probe.
 56. The apparatus ofclaim 51, wherein a point of detection is set through any of a starringoptical detector or a fiber-optic probe.
 57. The apparatus of claim 41wherein said means for analysis comprises a digital processor programmedto perform an age estimation procedure; wherein said digital value ispassed to said relative age estimation procedure and whereby a relativeage estimation is performed.
 58. In an apparatus for non-invasivelyestimating the relative age of a subject, an optical interfacecomprising: means for transmitting NIR energy towards a targetmeasurement site on a subject; and means for collecting NIR energyreflected from said measurement site; wherein said optical interface isadapted to be positioned at optimally determined distances from saidsite.
 59. The optical interface of claim 58, wherein said transmissionmeans is a light pipe, a fiber-optic probe, a lens system, or alight-detecting mirror.
 60. The optical interface of claim 58, whereinsaid energy collecting means comprises at least one starring opticaldetector.
 61. The optical interface of claim 58, wherein said energycollecting means comprises at least one fiber-optic probe.
 62. Theoptical interface of claim 58, wherein said optimal distances arespecified according to layer of the skin tissue to be sampled.
 63. Theoptical interface of claim 62, wherein said optimal distance isapproximately 0-300 μm fore the upper dermis.
 64. The optical interfaceof claim 62, wherein said optimal distance is approximately 0.3 mm-3 mmfor the lower dermis.
 65. The optical interface of claim 58, wherein apoint of Illumination is set through any of a focusing lens and afiber-optic probe.
 66. The optical interface of claim 58, wherein apoint of detection is set